Balancing Privacy and Utility in Cross-Company Defect Prediction

Peters, Fayola; Gong, Lina; Zhang, Hongyu

doi:10.1109/tse.2013.6

Cited by 126 publications

(88 citation statements)

References 52 publications

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“…CPDP is useful because for many companies that are relatively small or have new products, local data may not be readily available. With the use of better selection tools for training data and transfer learning techniques, researchers found it possible to predict defects for "data starved" software projects by using data from external sources [16]- [24].…”

Section: Transfer Learning and Prediction Modelsmentioning

confidence: 99%

Text Filtering and Ranking for Security Bug Report Prediction

Peters

Tun

et al. 2019

IIEEE Trans. Software Eng.

Self Cite

104

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Abstract-Security bug reports can describe security critical vulnerabilities in software products. Bug tracking systems may contain thousands of bug reports, where relatively few of them are security related. Therefore finding unlabelled security bugs among them can be challenging. To help security engineers identify these reports quickly and accurately, text-based prediction models have been proposed. These can often mislabel security bug reports due to a number of reasons such as class imbalance, where the ratio of non-security to security bug reports is very high. More critically, we have observed that the presence of security related keywords in both security and non-security bug reports can lead to the mislabelling of security bug reports. This paper proposes FARSEC, a framework for filtering and ranking bug reports for reducing the presence of security related keywords. Before building prediction models, our framework identifies and removes non-security bug reports with security related keywords. We demonstrate that FARSEC improves the performance of text-based prediction models for security bug reports in 90% of cases. Specifically, we evaluate it with 45,940 bug reports from Chromium and four Apache projects. With our framework, we mitigate the class imbalance issue and reduce the number of mislabelled security bug reports by 38%.

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Section: Transfer Learning and Prediction Modelsmentioning

confidence: 99%

Text Filtering and Ranking for Security Bug Report Prediction

Peters

Tun

et al. 2019

IIEEE Trans. Software Eng.

Self Cite

104

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“…Turhan et al showed that if we use all the data from a Training Data Set (TDS) -an aggregate of multiple data-sets, then the resulting defect predictor will have excessive false alarms [2]. A more recent study by Peters et al [12] demonstrated that if we used all the data from a TDS, then false alarms and recall would be low.…”

Section: Introductionmentioning

confidence: 99%

Better cross company defect prediction

Peters

Menzies

Marcus

2013

2013 10th Working Conference on Mining Software Repositories (MSR)

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162

161

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Abstract-How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach?This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Accordingly, this filter selects training data via the structure of other projects.To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction. Withincompany learning and cross-company learning with the Burak filter (the state-of-the-art relevancy filter). This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company; and 3) the Peters filter builds 64% more useful predictors than both withincompany and the Burak filter+cross-company approaches. Hence, we recommend the Peters filter for cross-company learning.

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“…Cross‐project fault prediction refers to predicting faults in a project using prediction models trained from historical data of other projects . In recent years, we have witnessed a lot of interest in developing new CPFP methods.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous fault prediction with cost‐sensitive domain adaptation

Jing

Zhu

2018

Software Testing Verif & Rel

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Summary In the early phases of software testing, projects may have only limited historical defect data. Learning prediction model with such insufficient training data will limit the efficacy of learned predictor. In practice, there are usually many publicly available fault prediction datasets. Recently, heterogeneous fault prediction (HFP) has been proposed. However, existing HFP models do not investigate how to use mixed project data to predict target. Furthermore, defect data are often imbalanced. The imbalanced data distribution of source usually leads to serious misclassification of fault‐prone instances, which will degrade the predictor's performance. Existing HFP methods do not consider the class imbalance problem in the training stages. In this paper, we propose a novel Cost‐sensitive Label and Structure‐consistent Unilateral Projection (CLSUP) approach for HFP. CLSUP can not only make better use of the within‐project and cross‐project data but also alleviate the class imbalance problem by setting different misclassification costs for fault‐prone and non–fault‐prone instances. Extensive experiments on 30 projects demonstrate the effectiveness of CLSUP.

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Balancing Privacy and Utility in Cross-Company Defect Prediction

Cited by 126 publications

References 52 publications

Text Filtering and Ranking for Security Bug Report Prediction

Text Filtering and Ranking for Security Bug Report Prediction

Better cross company defect prediction

Heterogeneous fault prediction with cost‐sensitive domain adaptation

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